Beyond oversight

As organizations transition to cloud-native architectures, understanding all that is happening across the entire infrastructure estate has proven to be a task beyond the capabilities of traditional monitoring. The inadequacy of monitoring has been underlined by the growing use of GenAI and by the increasingly sophisticated nature of cybersecurity attacks.  

Instead, organizations are turning to observability to gain actionable, real-time insights into these complex, distributed systems. Observability enables teams to understand, troubleshoot, and optimize performance by analyzing the huge volumes of MELT (metrics, events, logs, and traces) data necessary to maintain service, improve performance, and ensure business continuity.  

Observability's core value lies in reducing downtime through faster root cause analysis, preventing issues before they impact users, and managing ‘unknown unknowns’. As observability matures, its role is expanding to drive digital resiliency across the organization. Splunk’s most recent ‘State of Observability’ report2 found that 67% of respondents saw improved Mean Time to Resolution (MTTR) and 47% said that alerts significantly influenced security decision-making. And the benefits of this are much more than merely technical: 75% of respondents felt that observability both boosted employee productivity and linked to better customer experience, while 65% considered that their observability practice positively affects their revenues.  

Despite the criticality of this issue, a recent study found that only 11% of organizations described their observability practices as ‘expert’ (while a further 49% described them as ‘mature’). A separate study found that only 10% of respondents said they have full observability, i.e., they could observe the real-time status of every component of the entire technology stack.   

The consequences of this lack of expertise are significant. Tool sprawl – the reliance on multiple, uncoordinated monitoring and management tools – is one common result, and 84% of organizations are actively pursuing or considering tool consolidation. Many organizations also find they are overwhelmed by the data volumes required, making it difficult to separate the signal from the noise. And this problem is likely to get worse with the adoption of AI agents for operational tasks: as agents increasingly (and autonomously) navigate multi-stage workflows, this introduces ‘black box’ scenarios that make the decisions behind specific actions opaque to traditional observability approaches. This will require AI-powered observability (AI observing AI) to manage the volume and rate of agentic operations.  

The net of this is considerable unhappiness with the status quo: 59% are dissatisfied with their ability to obtain insights, and 67% are likely to switch observability platforms within 1-2 years4. There is also a huge focus on cost reduction, with 91% of respondents striving to reduce their observability spend, whether through better visibility of their monitoring costs (52%), adapting data management practices based on costs (38%), or collecting less monitoring data (32%). 

Digital infrastructures like Evolution Platform are critical enablers of the customer, employee, and operational experiences on which every business depends. Comprehensive, full-stack observability is therefore necessary to ensure the seamless, consistent, and secure access to digital services that these experiences require. To achieve this, organizations must embrace AI-driven, proactive, and predictive operations, replacing siloed, reactive monitoring with AIOps, automation, and accelerated root-cause resolution. This converges security intelligence and business context to deliver actionable insights that align with key business outcomes. Such integration is essential for identifying component interactions at scale and swiftly spotting security issues and performance bottlenecks that could affect application experience.

As threats evolve – from advanced cyberattacks to the risks posed by artificial intelligence – teams must be equipped to adapt quickly and confidently. A converged approach that unites Network Operations Center (NOC) and Operations Center (SOC) data empowers businesses to navigate these challenges, promoting resilience and driving operational transformation. By using AI and Machine Learning to analyze vast troves of MELT data, IT teams are empowered to resolve issues more efficiently, minimize downtime, and optimize resource allocation. The result is a faster, smarter decision-making process that adapts to the evolving demands of GenAI workloads and the broader business environment. 

Maintaining uptime, safeguarding sensitive data, and fostering innovation require a robust foundation across every layer of the IT stack. High-performance organizations leverage observability and security to ensure uninterrupted operations, protect assets, and accelerate growth. By collecting granular data on system performance, network dynamics, and device interactions, you can gain actionable intelligence that reinvents security operations. This unified observability ensures that GenAI and other applications are not only meeting performance targets but are also operating harmoniously alongside other business-critical services 

Whether it is critical outages, sophisticated cyber threats, or unexpected global events, businesses must be able to master any moment – no matter what happens. Observability enables organizations to conduct holistic impact analysis that assesses how individual workloads – including GenAI applications – are influencing network capacity and the performance of existing operations. By identifying potential resource contention early, organizations can intervene proactively, prioritizing incidents based on business impact to maintain a consistent and high-quality user experience for all services. In addition, proactive intelligence helps mitigate risks from configuration errors to unforeseen incidents, ensuring continuity and confidence. 

Together with Splunk and Cisco, Orange Business (including Orange Cyberdefense) creates the ultimate foundation for digital resilience: total visibility, proactive detection, and automated recovery. We provide full-stack, cross-domain observability – across networks, cloud, applications, digital experience, and security – with embedded security intelligence and business context. This converged approach replaces siloed monitoring tools to promote collaboration between ITOps, NetOps, SecOps, and business teams, eliminating blind spots and providing business-driven observability and resilience.  

This approach simplifies data management by providing flexible, high-throughput ingestion of structured and unstructured data, enabling real-time filtering, parsing, and routing. This reduces license costs by dropping unnecessary data, masking sensitive information, and optimizing storage, allowing for faster indexing and improved search performance.  

The Power of Three creates a single operational truth by correlating IT, network, cloud, and security data to reduce ‘alert noise and fatigue’, delivering cross-domain correlation that groups related alerts regardless of origin. This approach embraces AI-driven, proactive, and predictive operations with AIOps, automation, and accelerated root-cause resolution to enhance digital and cyber resilience, improving service availability, performance, compliance, and threat response.